The process of recognizing one or more objects or patterns in an image is object or pattern recognition (e.g., optical character recognition, face detection, vehicle detection, etc.). Object recognition has applications in a variety of fields, such as automated manufacturing, biomedical engineering, security, and document analysis.
Optical character recognition (OCR) consists of recognizing a string of characters in an image and returning a corresponding string of characters (e.g., in text form). OCR has a wide range of applications including the recognition of vehicle license plate numbers (e.g., for use in automated traffic law enforcement, surveillance, access control, tolls, etc.), the recognition of serial numbers on parts in an automated manufacturing environment, the recognition of labels on packages for routing purposes, and various document analysis applications.
The utilization of object recognition for a machine vision system is challenging due to image issues such as: changes in character angle with respect to a string, aspect ratio, scale, skew, lighting (e.g., non-uniform, reflection), and overlapping images.
Thus, a need exists to improve the determination of a class associated with an image as described herein.